Ibbaka has developed a model for the lifecycle for managing skills in an organization.  We developed this lifecycle model to help our customers and partners think about what is needed to develop a truly robust picture of skills in their organizations and then to act on those insights.

Skill management is not simply ensuring that current systems can accommodate an additional layer of metadata around existing data records (objects), such as the employee record in an HRIS, a job record in an applicant tracking system, or learning objects in an LMS or LXP. Skills themselves are a unique entity with their own properties. Skills can be categorized, defined, target proficiencies established, peer assessments carried out and so on. One of the most important properties of skills is how skills connect to each other, connect people, and underlie performance.

In order for skill data to provide an ongoing advantage to the business, we need to be moving people (and skill data) across the following stages to drive real value.

1. Skill identification

Everyone in the organization needs to be empowered to identify or discover skills that are critical to their work. They also need to be able to define and assess these skills. There are many ways to d o this. Documents can be uploaded; links can be made to other systems; onboarding wizards can be used. The goal is to give the individual a sense of control and to have them recognize their own skills, the skills of the people around them, and to get insights.

2. Match talent to opportunity, based on skill data

One reason organizations are racing to embrace skills as ‘the next big dataset’ is because of the power it unleases to discover potential in your organization. When individuals are empowered to identify and assess all of the skills they have (beyond the ones that are commonly associated with their current job title), it opens up the door to discovering talent.
Opportunity matching goes both ways - individuals need to know how their current skill set holds up against the next job they want, and managers need to be able to use skill data in various contexts (roles, jobs, teams) to quickly find people who can support the organization’s goals.

3. Upskill and enhance skill data

At Ibbaka, we’re big fans of the 70/20/10 model of learning. This model of learning states that 70% of skill development happens as a result of doing work, 20% is social, and only about 10% of real learning happens as a result of formal training.
In an ideal world, this means that opportunity matching should be a primary means by which individuals upskill. If the skills they are using can be captured and assessed in the context of that work (in teams or roles) then the projects and work people are doing become the means by which skill data is maintained.
If skill data is only being enhanced as a result of course completions or formal learning opportunities, skill data ends up being far less reliable if for no other reason than a demonstrating understanding does not necessarily lead directly to an ability to apply skills.

4. Measure growth (and change over time)

This is the hardest part, because we don’t just need to measure individual growth in skill proficiency. We also need to understand how skill growth contributes to organizational growth,and how it can be linked to outcomes. Said another way, this is really about understanding which skills and skill investments that are driving results.
We currently think about measuring growth in terms of:

• Coverage: How many people have a given skill

• Utilization: How widely is the skill being applied in teams and/or in job roles

• Proficiency: Are individual and collective levels of proficiency going up?

Ibbaka uses the skill management lifecycle to support our clients and ensure customer success.

We also use it to guide our product roadmap and we’d love to hear your thoughts on the cycle itself, as well as which of the stages you find most complex or challenging, and why. If you’d like to get in touch for a conversation, please contact us at info@ibbaka.com.